kikuyu_translategemma_12b_merged_V2

Uploaded finetuned model

• Developed by: gateremark
• License: apache-2.0
• Finetuned from model : google/translategemma-12b-it

This gemma3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

Kikuyu TranslateGemma-12B

Fine-tuned English → Kikuyu translation model based on Google's TranslateGemma-12B-it.

🌍 Live Demo: c-elo.com/c-elo-ai (Talk to our AI)

Learn about our training process and challenges faced

Model Details

Usage

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "gateremark/kikuyu_translategemma_12b_merged_V2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def translate_to_kikuyu(text: str) -> str:
    messages = [
        {
            "role": "user",
            "content": [{
                "type": "text",
                "source_lang_code": "en",
                "target_lang_code": "ki",
                "text": text
            }]
        }
    ]
    
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)
    
    terminators = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<end_of_turn>"),
    ]
    
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            max_new_tokens=256,
            temperature=0.3,
            do_sample=True,
            eos_token_id=terminators,
        )
    
    response = tokenizer.decode(
        outputs[0][input_ids.shape[1]:],
        skip_special_tokens=True
    )
    return response.strip()

# Example
print(translate_to_kikuyu("Hello, how are you?"))
# Output: Hihi, ũrĩ atĩa?

Using with Unsloth (Faster Inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="gateremark/kikuyu_translategemma_12b_merged_V2",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,  # Optional: reduce VRAM to ~6GB
)

Training Details

Dataset

• Source: gateremark/english-kikuyu-translations
• Size: 30,430 parallel sentences
• Split: 95% train (28,908) / 5% eval (1,522)

Hyperparameters

Target Modules

["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

Hardware

• GPU: NVIDIA H200 (139GB VRAM)
• Platform: Lightning.ai
• Training Time: ~90 minutes

Evaluation

Sample Translations

Limitations

• Direction: English → Kikuyu only (reverse not trained)
• Domain: General text; may struggle with technical/specialized content
• Dialects: Trained on standard Kikuyu; dialect variations not covered
• Evaluation: BLEU-based; human evaluation in progress

Intended Use

• Translation tools for Kikuyu speakers
• Language learning applications
• Research on low-resource African language NLP
• Cultural preservation initiatives

Citation

@misc{gatere2026kikuyutranslategemma,
  author = {Mark Gatere},
  title = {Kikuyu TranslateGemma-12B: Fine-tuning TranslateGemma for Low-Resource Bantu Language Translation},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/gateremark/kikuyu_translategemma_12b_merged_V2}}
}

Acknowledgments

• Google for the TranslateGemma base model
• Unsloth for 2x faster fine-tuning
• Lightning.ai for GPU compute